Browsing by Author "Wang, Nian"

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    Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging
    (Frontiers Media, 2022-08-17) Maharjan, Surendra; Tsai, Andy P.; Lin, Peter B.; Ingraham, Cynthia; Jewett, Megan R.; Landreth, Gary E.; Oblak, Adrian L.; Wang, Nian; Radiology and Imaging Sciences, School of Medicine
    Purpose: To evaluate the age-dependent microstructure changes in 5xFAD mice using high-resolution diffusion tensor imaging (DTI). Methods: The 5xFAD mice at 4, 7.5, and 12 months and the wild-type controls at 4 months were scanned at 9.4T using a 3D echo-planar imaging (EPI) pulse sequence with the isotropic spatial resolution of 100 μm. The b-value was 3000 s/mm2 for all the diffusion MRI scans. The samples were also acquired with a gradient echo pulse sequence at 50 μm isotropic resolution. The microstructure changes were quantified with DTI metrics, including fractional anisotropy (FA) and mean diffusivity (MD). The conventional histology was performed to validate with MRI findings. Results: The FA values (p = 0.028) showed significant differences in the cortex between wild-type (WT) and 5xFAD mice at 4 months, while hippocampus, anterior commissure, corpus callosum, and fornix showed no significant differences for either FA and MD. FA values of 5xFAD mice gradually decreased in cortex (0.140 ± 0.007 at 4 months, 0.132 ± 0.008 at 7.5 months, 0.126 ± 0.013 at 12 months) and fornix (0.140 ± 0.007 at 4 months, 0.132 ± 0.008 at 7.5 months, 0.126 ± 0.013 at 12 months) with aging. Both FA (p = 0.029) and MD (p = 0.037) demonstrated significant differences in corpus callosum between 4 and 12 months age old. FA and MD were not significantly different in the hippocampus or anterior commissure. The age-dependent microstructure alterations were better captured by FA when compared to MD. Conclusion: FA showed higher sensitivity to monitor amyloid deposition in 5xFAD mice. DTI may be utilized as a sensitive biomarker to monitor beta-amyloid progression for preclinical studies.
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    Aging x Environment x genetic risk for late onset Alzheimer’s disease results in alterations in cognitive function in mice independent of amyloid and tau pathology
    (Wiley, 2025-01-03) Williams, Sean-Paul Gerard; Santos, Diogo Francisco Silva; Haynes, Kathryn A.; Heaton, Nicholas; Hart, Jason T.; Kotredes, Kevin P.; Pandey, Ravi S.; Persohn, Scott C.; Eldridge, Kierra; Ingraham, Cynthia M.; Lloyd, Christopher D.; Wang, Nian; Sasner, Michael; Carter, Gregory W.; Territo, Paul R.; Lamb, Bruce T.; Howell, Gareth R.; Oblak, Adrian L.; Sukoff Rizzo, Stacey J.; Neurology, School of Medicine
    Background: Alzheimer’s disease (AD) research has been historically dominated with studies in mouse models expressing familial AD mutations; however, the majority of AD patients have the sporadic, late‐onset form of AD (LOAD). To address this gap, the IU/JAX/PITT MODEL‐AD Consortium has focused on development of mouse models that recapitulate LOAD by combining genetic risk variants with environmental risk factors and aging to enable more precise models to evaluate potential therapeutics. The present studies were undertaken to characterize cognitive and neurophysiological phenotypes in LOAD mice. Method: Two genetic risk factors, APOE4 and Trem2*R47H, were incorporated into C57BL/6J mice with humanized amyloid‐beta to produce the LOAD2 model (JAX# 030670). Male and female LOAD2 and WT mice were exposed to ad libitum 45% high‐fat diet from 2‐months of age (LOAD2+HFD or WT+HFD, respectively) throughout their lifespan and compared to LOAD2 and WT mice on control diet (+CD). Cognitive training began at 14‐months of age using a touchscreen testing battery, similar to previously described methods (Oomen et al 2013). At the conclusion of touchscreen testing, subjects were implanted with wireless telemetry devices (DSI) for evaluation of electroencephalography (EEG) signatures. Result: All subjects met the touch‐reward association criteria. During task acquisition LOAD2+CD mice demonstrated impaired acquisition relative to WT+CD, while both LOAD2+HFD and WT+HFD failed to learn the task as indicated by accuracy less than chance (<50%); which was confirmed in a separate cohort. LOAD2+HFD mice demonstrated increased spikewave events as measured by EEG, relative to LOAD2+CD. At 18‐months of age +CD mice that met acquisition criteria were evaluated in a location discrimination task with LOAD2+CD mice demonstrating modest impairments in pattern separation relative to age‐matched WT+CD. Conclusion: These data are the first reports of cognitive deficits and neurophysiological alterations in mice with environmental x genetic risk for LOAD, independent of amyloid and tau pathology. Importantly, the present findings demonstrate the sensitivity of the translational touchscreen testing battery for detecting mild cognitive impairment in LOAD mice with corresponding neurophysiologic alterations, and extend previous characterization data for the LOAD2 model and its utility for the study of the biology of LOAD.
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    Characterizing Molecular and Synaptic Signatures in mouse models of Late-Onset Alzheimer’s Disease Independent of Amyloid and Tau Pathology
    (bioRxiv, 2023-12-20) Kotredes, Kevin P.; Pandey, Ravi S.; Persohn, Scott; Elderidge, Kierra; Burton, Charles P.; Miner, Ethan W.; Haynes, Kathryn A.; Santos, Diogo Francisco S.; Williams, Sean-Paul; Heaton, Nicholas; Ingraham, Cynthia M.; Lloyd, Christopher; Garceau, Dylan; O’Rourke, Rita; Herrick, Sarah; Rangel-Barajas, Claudia; Maharjan, Surendra; Wang, Nian; Sasner, Michael; Lamb, Bruce T.; Territo, Paul R.; Sukoff Rizzo, Stacey J.; Carter, Gregory W.; Howell, Gareth R.; Oblak, Adrian L.; Medical and Molecular Genetics, School of Medicine
    Introduction: MODEL-AD is creating and distributing novel mouse models with humanized, clinically relevant genetic risk factors to more accurately mimic LOAD than commonly used transgenic models. Methods: We created the LOAD2 model by combining APOE4, Trem2*R47H, and humanized amyloid-beta. Mice aged up to 24 months were subjected to either a control diet or a high-fat/high-sugar diet (LOAD2+HFD) from two months of age. We assessed disease-relevant outcomes, including in vivo imaging, biomarkers, multi-omics, neuropathology, and behavior. Results: By 18 months, LOAD2+HFD mice exhibited cortical neuron loss, elevated insoluble brain Aβ42, increased plasma NfL, and altered gene/protein expression related to lipid metabolism and synaptic function. In vivo imaging showed age-dependent reductions in brain region volume and neurovascular uncoupling. LOAD2+HFD mice also displayed deficits in acquiring touchscreen-based cognitive tasks. Discussion: Collectively the comprehensive characterization of LOAD2+HFD mice reveal this model as important for preclinical studies that target features of LOAD independent of amyloid and tau.
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    Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging
    (Wiley, 2025) Jelescu, Ileana O.; Grussu, Francesco; Ianus, Andrada; Hansen, Brian; Barrett, Rachel L. C.; Aggarwal, Manisha; Michielse, Stijn; Nasrallah, Fatima; Syeda, Warda; Wang, Nian; Veraart, Jelle; Roebroeck, Alard; Bagdasarian, Andrew F.; Eichner, Cornelius; Sepehrband, Farshid; Zimmermann, Jan; Soustelle, Lucas; Bowman, Christien; Tendler, Benjamin C.; Hertanu, Andreea; Jeurissen, Ben; Verhoye, Marleen; Frydman, Lucio; van de Looij, Yohan; Hike, David; Dunn, Jeff F.; Miller, Karla; Landman, Bennett A.; Shemesh, Noam; Anderson, Adam; McKinnon, Emilie; Farquharson, Shawna; Dell'Acqua, Flavio; Pierpaoli, Carlo; Drobnjak, Ivana; Leemans, Alexander; Harkins, Kevin D.; Descoteaux, Maxime; Xu, Duan; Huang, Hao; Santin, Mathieu D.; Grant, Samuel C.; Obenaus, Andre; Kim, Gene S.; Wu, Dan; Le Bihan, Denis; Blackband, Stephen J.; Ciobanu, Luisa; Fieremans, Els; Bai, Ruiliang; Leergaard, Trygve B.; Zhang, Jiangyang; Dyrby, Tim B.; Johnson, G. Allan; Cohen-Adad, Julien; Budde, Matthew D.; Schilling, Kurt G.; Neurology, School of Medicine
    Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
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    Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2-Ex vivo imaging: Added value and acquisition
    (Wiley, 2025) Schilling, Kurt G.; Grussu, Francesco; Ianus, Andrada; Hansen, Brian; Howard, Amy F. D.; Barrett, Rachel L. C.; Aggarwal, Manisha; Michielse, Stijn; Nasrallah, Fatima; Syeda, Warda; Wang, Nian; Veraart, Jelle; Roebroeck, Alard; Bagdasarian, Andrew F.; Eichner, Cornelius; Sepehrband, Farshid; Zimmermann, Jan; Soustelle, Lucas; Bowman, Christien; Tendler, Benjamin C.; Hertanu, Andreea; Jeurissen, Ben; Verhoye, Marleen; Frydman, Lucio; van de Looij, Yohan; Hike, David; Dunn, Jeff F.; Miller, Karla; Landman, Bennett A.; Shemesh, Noam; Anderson, Adam; McKinnon, Emilie; Farquharson, Shawna; Dell'Acqua, Flavio; Pierpaoli, Carlo; Drobnjak, Ivana; Leemans, Alexander; Harkins, Kevin D.; Descoteaux, Maxime; Xu, Duan; Huang, Hao; Santin, Mathieu D.; Grant, Samuel C.; Obenaus, Andre; Kim, Gene S.; Wu, Dan; Le Bihan, Denis; Blackband, Stephen J.; Ciobanu, Luisa; Fieremans, Els; Bai, Ruiliang; Leergaard, Trygve B.; Zhang, Jiangyang; Dyrby, Tim B.; Johnson, G. Allan; Cohen-Adad, Julien; Budde, Matthew D.; Jelescu, Ileana O.; Neurology, School of Medicine
    The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
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    Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3-Ex vivo imaging: Data processing, comparisons with microscopy, and tractography
    (Wiley, 2025) Schilling, Kurt G.; Howard, Amy F. D.; Grussu, Francesco; Ianus, Andrada; Hansen, Brian; Barrett, Rachel L. C.; Aggarwal, Manisha; Michielse, Stijn; Nasrallah, Fatima; Syeda, Warda; Wang, Nian; Veraart, Jelle; Roebroeck, Alard; Bagdasarian, Andrew F.; Eichner, Cornelius; Sepehrband, Farshid; Zimmermann, Jan; Soustelle, Lucas; Bowman, Christien; Tendler, Benjamin C.; Hertanu, Andreea; Jeurissen, Ben; Verhoye, Marleen; Frydman, Lucio; van de Looij, Yohan; Hike, David; Dunn, Jeff F.; Miller, Karla; Landman, Bennett A.; Shemesh, Noam; Anderson, Adam; McKinnon, Emilie; Farquharson, Shawna; Dell'Acqua, Flavio; Pierpaoli, Carlo; Drobnjak, Ivana; Leemans, Alexander; Harkins, Kevin D.; Descoteaux, Maxime; Xu, Duan; Huang, Hao; Santin, Mathieu D.; Grant, Samuel C.; Obenaus, Andre; Kim, Gene S.; Wu, Dan; Le Bihan, Denis; Blackband, Stephen J.; Ciobanu, Luisa; Fieremans, Els; Bai, Ruiliang; Leergaard, Trygve B.; Zhang, Jiangyang; Dyrby, Tim B.; Johnson, G. Allan; Cohen-Adad, Julien; Budde, Matthew D.; Jelescu, Ileana O.; Neurology, School of Medicine
    Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.
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    Corrigendum: Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging
    (Frontiers Media, 2022-10-13) Maharjan, Surendra; Tsai, Andy P.; Lin, Peter B.; Ingraham, Cynthia; Jewett, Megan R.; Landreth, Gary E.; Oblak, Adrian L.; Wang, Nian; Radiology and Imaging Sciences, School of Medicine
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    Effects of Angular Resolution and b Value on Diffusion Tensor Imaging in Knee Joint
    (Sage, 2021) Zhao, Qi; Ridout, Rees P.; Shen, Jikai; Wang, Nian; Radiology and Imaging Sciences, School of Medicine
    Objective: To investigate the influences of the diffusion gradient directions (angular resolution) and the strength of the diffusion gradient (b value) on diffusion tensor imaging (DTI) metrics and tractography of various connective tissues in knee joint. Design: Two rat knee joints were scanned on a preclinical 9.4-T system using a 3-dimensional diffusion-weighted spin echo pulse sequence. One protocol with b value of 500, 1500, and 2500 s/mm2 were acquired separately using 43 diffusion gradient directions. The other protocol with b value of 1000 s/mm2 was performed using 147 diffusion gradient directions. The in-plane resolution was 45 µm isotropic. Fractional anisotropy (FA) and mean diffusivity (MD) were compared at different angular resolution. Tractography was quantitatively evaluated at different b values and angular resolutions in cartilage, ligament, meniscus, and growth plate. Results: The ligament showed higher FA value compared with growth plate and cartilage. The FA values were largely overestimated at the angular resolution of 6. Compared with FA, MD showed less sensitivity to the angular resolution. The fiber tracking was failed at low angular resolution (6 diffusion gradient directions) or high b value (2500 s/mm2). The quantitative measurements of tract length and track volume were strongly dependent on angular resolution and b value. Conclusions: To obtain consistent DTI outputs and tractography in knee joint, the scan may require a proper b value (ranging from 500 to 1500 s/mm2) and sufficient angular resolution (>14) with signal-to-noise ratio >10.
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    Genetic Variants of Phospholipase C-γ2 Alter the Phenotype and Function of Microglia and Confer Differential Risk for Alzheimer’s Disease
    (Elsevier, 2023) Tsai, Andy P.; Dong, Chuanpeng; Lin, Peter Bor-Chian; Oblak, Adrian L.; Di Prisco, Gonzalo Viana; Wang, Nian; Hajicek, Nicole; Carr, Adam J.; Lendy, Emma K.; Hahn, Oliver; Atkins, Micaiah; Foltz, Aulden G.; Patel, Jheel; Xu, Guixiang; Moutinho, Miguel; Sondek, John; Zhang, Qisheng; Mesecar, Andrew D.; Liu, Yunlong; Atwood, Brady K.; Wyss-Coray, Tony; Nho, Kwangsik; Bissel, Stephanie J.; Lamb, Bruce T.; Landreth, Gary E.; Medical and Molecular Genetics, School of Medicine
    Genetic association studies have demonstrated the critical involvement of the microglial immune response in Alzheimer's disease (AD) pathogenesis. Phospholipase C-gamma-2 (PLCG2) is selectively expressed by microglia and functions in many immune receptor signaling pathways. In AD, PLCG2 is induced uniquely in plaque-associated microglia. A genetic variant of PLCG2, PLCG2P522R, is a mild hypermorph that attenuates AD risk. Here, we identified a loss-of-function PLCG2 variant, PLCG2M28L, that confers an increased AD risk. PLCG2P522R attenuated disease in an amyloidogenic murine AD model, whereas PLCG2M28L exacerbated the plaque burden associated with altered phagocytosis and Aβ clearance. The variants bidirectionally modulated disease pathology by inducing distinct transcriptional programs that identified microglial subpopulations associated with protective or detrimental phenotypes. These findings identify PLCG2M28L as a potential AD risk variant and demonstrate that PLCG2 variants can differentially orchestrate microglial responses in AD pathogenesis that can be therapeutically targeted.
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    High angular resolution diffusion imaging (HARDI) of porcine menisci: a comparison of diffusion tensor imaging and generalized q-sampling imaging
    (AME, 2024) Zhao, Qi; Holt, Abigail; Spritzer, Charles E.; DeFrate, Louis E.; McNulty, Amy L.; Wang, Nian; Radiology and Imaging Sciences, School of Medicine
    Background: Diffusion magnetic resonance imaging (MRI) allows for the quantification of water diffusion properties in soft tissues. The goal of this study was to characterize the 3D collagen fiber network in the porcine meniscus using high angular resolution diffusion imaging (HARDI) acquisition with both diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI). Methods: Porcine menisci (n=7) were scanned ex vivo using a three-dimensional (3D) HARDI spin-echo pulse sequence with an isotropic resolution of 500 µm at 7.0 Tesla. Both DTI and GQI reconstruction techniques were used to quantify the collagen fiber alignment and visualize the complex collagen network of the meniscus. The MRI findings were validated with conventional histology. Results: DTI and GQI exhibited distinct fiber orientation maps in the meniscus using the same HARDI acquisition. We found that crossing fibers were only resolved with GQI, demonstrating the advantage of GQI over DTI to visualize the complex collagen fiber orientation in the meniscus. Furthermore, the MRI findings were consistent with conventional histology. Conclusions: HARDI acquisition with GQI reconstruction more accurately resolves the complex 3D collagen architecture of the meniscus compared to DTI reconstruction. In the future, these technologies have the potential to nondestructively assess both normal and abnormal meniscal structure.